NCP-GENL › Fine-Tuning

Fine-Tuning Practice Questions

This domain covers parameter-efficient adaptation of large language models: LoRA, QLoRA, and memory-saving techniques for constrained hardware. Questions test your understanding of how low-rank updates, quantized base weights, and gradient checkpointing trade compute, memory, and quality during fine-tuning, and how configuration choices like rank affect trainable parameters and model capacity.